Adapting machine translation education to the neural era : a case study of MT quality assessment
- Author
- Lieve Macken (UGent) , Bram Vanroy (UGent) and Arda Tezcan (UGent)
- Organization
- Project
- Abstract
- The use of automatic evaluation metrics to is well established in the translation industry. Whereas it is relatively easy to cover the word- and character-based metrics in an MT course, it is less obvious to integrate the newer neural metrics. In this paper we discuss how we introduced the topic of MT quality assessment in a course for translation students. We selected three English source texts, each having a different difficulty level and style, and let the students translate the texts into their L1 and reflect upon translation difficulty. Afterwards, the students were asked to assess MT quality for the same texts using different methods and to critically reflect upon obtained results. The students had access to the MATEO web interface, which contains wordand character-based metrics as well as neural metrics. The students used two different reference translations: their own translations and professional translations of the three texts. We not only synthesise the comments of the students, but also present the results of some cross-lingual analyses on nine different language pairs.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01H293RH312H0AGSYSZW7Q7P1H
- MLA
- Macken, Lieve, et al. “Adapting Machine Translation Education to the Neural Era : A Case Study of MT Quality Assessment.” Proceedings of the 24th Annual Conference of the European Association for Machine Translation, edited by Mary Nurminen et al., European Association for Machine Translation (EAMT), 2023, pp. 305–14.
- APA
- Macken, L., Vanroy, B., & Tezcan, A. (2023). Adapting machine translation education to the neural era : a case study of MT quality assessment. In M. Nurminen, J. Brenner, M. Koponen, S. Latomaa, M. Mikhailov, F. Schierl, … H. Moniz (Eds.), Proceedings of the 24th Annual Conference of the European Association for Machine Translation (pp. 305–314). European Association for Machine Translation (EAMT).
- Chicago author-date
- Macken, Lieve, Bram Vanroy, and Arda Tezcan. 2023. “Adapting Machine Translation Education to the Neural Era : A Case Study of MT Quality Assessment.” In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, edited by Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, et al., 305–14. European Association for Machine Translation (EAMT).
- Chicago author-date (all authors)
- Macken, Lieve, Bram Vanroy, and Arda Tezcan. 2023. “Adapting Machine Translation Education to the Neural Era : A Case Study of MT Quality Assessment.” In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, ed by. Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, and Helena Moniz, 305–314. European Association for Machine Translation (EAMT).
- Vancouver
- 1.Macken L, Vanroy B, Tezcan A. Adapting machine translation education to the neural era : a case study of MT quality assessment. In: Nurminen M, Brenner J, Koponen M, Latomaa S, Mikhailov M, Schierl F, et al., editors. Proceedings of the 24th Annual Conference of the European Association for Machine Translation. European Association for Machine Translation (EAMT); 2023. p. 305–14.
- IEEE
- [1]L. Macken, B. Vanroy, and A. Tezcan, “Adapting machine translation education to the neural era : a case study of MT quality assessment,” in Proceedings of the 24th Annual Conference of the European Association for Machine Translation, Tampere, Finland, 2023, pp. 305–314.
@inproceedings{01H293RH312H0AGSYSZW7Q7P1H,
abstract = {{The use of automatic evaluation metrics to is well established in the translation industry. Whereas it is relatively easy to cover the word- and character-based metrics in an MT course, it is less obvious to integrate the newer neural metrics. In this paper we discuss how we introduced the topic of MT quality assessment in a course for translation students. We selected three English source texts, each having a different difficulty level and style, and let the students translate the texts into their L1 and reflect upon translation difficulty. Afterwards, the students were asked to assess MT quality for the same texts using different methods and to critically reflect upon obtained results. The students had access to the MATEO web interface, which contains wordand character-based metrics as well as neural metrics. The students used two different reference translations: their own translations and professional translations of the three texts. We not only synthesise the comments of the students, but also present the results of some cross-lingual analyses on nine different language pairs.}},
author = {{Macken, Lieve and Vanroy, Bram and Tezcan, Arda}},
booktitle = {{Proceedings of the 24th Annual Conference of the European Association for Machine Translation}},
editor = {{Nurminen, Mary and Brenner, Judith and Koponen, Maarit and Latomaa, Sirkku and Mikhailov, Mikhail and Schierl, Frederike and Ranasinghe, Tharindu and Vanmassenhove, Eva and Alvarez Vidal, Sergi and Aranberri, Nora and Nunziatini, Mara and Parra Escartín, Carla and Forcada, Mikel and Popovic, Maja and Scarton, Carolina and Moniz, Helena}},
isbn = {{9789520329471}},
language = {{eng}},
location = {{Tampere, Finland}},
pages = {{305--314}},
publisher = {{European Association for Machine Translation (EAMT)}},
title = {{Adapting machine translation education to the neural era : a case study of MT quality assessment}},
url = {{https://events.tuni.fi/eamt23/proceedings/}},
year = {{2023}},
}